@article{4b9ebd59455245dfb1d893c9931deab8,
title = "Stm: An R package for structural topic models",
abstract = "This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.",
keywords = "LDA, R, Stm, Structural topic model, Text analysis",
author = "Roberts, {Margaret E.} and Stewart, {Brandon M.} and Dustin Tingley",
note = "Funding Information: We thank Antonio Coppola, Jetson Leder-Luis, Christopher Lucas, and Alex Storer for various assistance in the construction of this package. We also thank the many package users who have contributed through bug reports and feature requests. We extend particular gratitude to users on Github who have contributed code via pull requests including Ken Benoit, Patrick Perry, Chris Baker, Jeffrey Arnold, Thomas Leeper, Vincent Arel-Bundock, Santiago Castro, Rose Hartman, Vineet Bansal and Github user sw1. We gratefully acknowledge grant support from the Spencer Foundation{\textquoteright}s New Civics initiative, the Hewlett Foundation, a National Science Foundation grant under the Resource Implementations for Data Intensive Research program, Princeton{\textquoteright}s Center for Statistics and Machine Learning, and The Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number P2CHD047879. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding institutions. Additional details and the development version are available at https://www. structuraltopicmodel.com/. Funding Information: We thank Antonio Coppola, Jetson Leder-Luis, Christopher Lucas, and Alex Storer for various assistance in the construction of this package. We also thank the many package users who have contributed through bug reports and feature requests. We extend particular gratitude to users on Github who have contributed code via pull requests including Ken Benoit, Patrick Perry, Chris Baker, Jeffrey Arnold, Thomas Leeper, Vincent Arel-Bundock, Santiago Castro, Rose Hartman, Vineet Bansal and Github user sw1. We gratefully acknowledge grant support from the Spencer Foundation?s New Civics initiative, the Hewlett Foundation, a National Science Foundation grant under the Resource Implementations for Data Intensive Research program, Princeton?s Center for Statistics and Machine Learning, and The Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number P2CHD047879. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding institutions. Additional details and the development version are available at https://www. structuraltopicmodel.com/. Publisher Copyright: {\textcopyright} 2019, American Statistical Association. All rights reserved.",
year = "2019",
doi = "10.18637/jss.v091.i02",
language = "English (US)",
volume = "91",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
}